In recent years biological processes modeling and simulation have become two key issues in analyzing complex cellular systems. Information about metabolic networks is often incomplete, since a large portion of available data is ignored by its probabilistic nature. The main objective of this work is to investigate metabolic networks behavior in terms of their fault tolerance capabilities as random removal of network nodes and high-connectivity-degree node deletion aimed at compromising or modifying network activity. This paper proposes a software framework, namely CEllDataLaB, containing three tasks to perform the structural and functional analysis: topological analysis, flux balance analysis and extreme pathway algorithm. The performed trials have shown that the node connectivity degrees as well as the node functional role in the network are key issues to evaluate the impact of node deletion on network behavior and activity. The metabolic network used in this work is related to the human hepatocyte metabolism.
|Numero di pagine||8|
|Stato di pubblicazione||Published - 2011|
All Science Journal Classification (ASJC) codes
- Artificial Intelligence